knitr::opts_chunk$set(
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
library(rio)
library(labelled)
library(codebook)
##
## Attaching package: 'codebook'
## The following object is masked from 'package:labelled':
##
## to_factor
codebook_data <- import("../data_processing/output_data/item_data/sr_item_data.csv")
var_label(codebook_data) <- list(
word = "Original stimulus showed to the participant in the language of the study.",
class = "Word/nonword trial type indicator, which tells you what the correct answer is for the trial.",
avgRT = "Average response latency for the stimulus item across all participants.",
avgZ_RT = "Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.",
samplesize = "The number of participants who were shown that item.",
n_answered = "The number of participants who answered that item (i.e., it did not time out.",
seRT = "The standard error of the raw response latency.",
seZ_RT = "The standard error of the z-scored response latency.",
accuracy = "The proportion of correct answers out of total answered for that item.",
Z2.5_avgRT = "The average raw response latency excluding outliers of 2.5 Z or higher.",
Z2.5_avgZ_RT = "The average Z-scored response latency excluding outliers of 2.5 Z or higher.",
Z2.5_samplesize = "The number of data points viewed that were not Z equal 2.5 or higher",
Z2.5_n_answered = "The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
Z2.5_seRT = "Standard error of the raw response latency after removing Z equal 2.5 or higher.",
Z2.5_seZ_RT = "Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.",
Z2.5_accuracy = "The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.",
Z3.0_avgRT = "The average raw response latency excluding outliers of 3.0 Z or higher.",
Z3.0_avgZ_RT = "The average Z-scored response latency excluding outliers of 3.0 Z or higher.",
Z3.0_samplesize = "The number of data points viewed that were not Z equal 3.0 or higher",
Z3.0_n_answered = "The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
Z3.0_seRT = "Standard error of the raw response latency after removing Z equal 3.0 or higher.",
Z3.0_seZ_RT = "Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.",
Z3.0_accuracy = "The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.")
metadata(codebook_data)$name <- "Semantic Priming Across Many Languages Item Level Data (Example using Serbian data)"
metadata(codebook_data)$description <- "This dataset includes the summarized item data from the SPAML project (example is specifically Serbian, but all files are structured the same way). The data is averaged over items, regardless of condition for words (i.e., related or unrelated trial).
Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages."
metadata(codebook_data)$identifier <- "https://doi.org/10.5281/zenodo.10888833"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Buchanan, E., Cuccolo, K., Heyman, T., Iyer, A., Coles, N., Lewis Jr, N., Peters, K., van Berkel, N., Taylor, J., Van't Veer, A. E., Montefinese, M., Valentine, K. D., Maxwell, N., Türkan, B. N., Williams, G., Oliveros-Chacana, J. C., Röer, J., Fini, C., Acar, O., … Lewis, S. C. (2024). SemanticPriming/SPAML: SPAML v1 Data Release (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10888833"
metadata(codebook_data)$url <- "https://github.com/SemanticPriming/SPAML/releases/"
metadata(codebook_data)$datePublished <- "2024-05-01"
metadata(codebook_data)$temporalCoverage <- "2022-2024"
metadata(codebook_data)$spatialCoverage <- "Online"
codebook(codebook_data)
Dataset name: Semantic Priming Across Many Languages Item Level Data (Example using Serbian data)
This dataset includes the summarized item data from the SPAML project (example is specifically Serbian, but all files are structured the same way). The data is averaged over items, regardless of condition for words (i.e., related or unrelated trial).
Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.
Temporal Coverage: 2022-2024
Spatial Coverage: Online
Citation: Buchanan, E., Cuccolo, K., Heyman, T., Iyer, A., Coles, N., Lewis Jr, N., Peters, K., van Berkel, N., Taylor, J., Van’t Veer, A. E., Montefinese, M., Valentine, K. D., Maxwell, N., Türkan, B. N., Williams, G., Oliveros-Chacana, J. C., Röer, J., Fini, C., Acar, O., … Lewis, S. C. (2024). SemanticPriming/SPAML: SPAML v1 Data Release (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10888833
Identifier: https://doi.org/10.5281/zenodo.10888833
Date published: 2024-05-01
Creator:
| name | value |
|---|---|
| 1 | Erin M. Buchanan |
|
#Variables
Original stimulus showed to the participant in the language of the study.
Distribution of values for word
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| word | Original stimulus showed to the participant in the language of the study. | character | 0 | 1 | 3917 | 0 | 2 | 20 | 0 |
Word/nonword trial type indicator, which tells you what the correct answer is for the trial.
Distribution of values for class
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| class | Word/nonword trial type indicator, which tells you what the correct answer is for the trial. | character | 0 | 1 | 2 | 0 | 4 | 7 | 0 |
Average response latency for the stimulus item across all participants.
Distribution of values for avgRT
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| avgRT | Average response latency for the stimulus item across all participants. | numeric | 0 | 1 | 383 | 854 | 1756 | 892.9244 | 194.4628 | ▁▇▃▁▁ |
Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.
Distribution of values for avgZ_RT
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| avgZ_RT | Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition. | numeric | 0 | 1 | -1.4 | -0.083 | 2.8 | 0.0194038 | 0.5490782 | ▂▇▃▁▁ |
The number of participants who were shown that item.
Distribution of values for samplesize
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| samplesize | The number of participants who were shown that item. | numeric | 0 | 1 | 1 | 124 | 366 | 125.7955 | 24.74073 | ▁▇▁▁▁ |
The number of participants who answered that item (i.e., it did not time out.
Distribution of values for n_answered
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| n_answered | The number of participants who answered that item (i.e., it did not time out. | numeric | 0 | 1 | 72 | 130 | 412 | 132.8486 | 23.89708 | ▇▂▁▁▁ |
The standard error of the raw response latency.
Distribution of values for seRT
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| seRT | The standard error of the raw response latency. | numeric | 1 | 0.9997447 | 0.84 | 30 | 143 | 31.91637 | 10.79077 | ▇▇▁▁▁ |
The standard error of the z-scored response latency.
Distribution of values for seZ_RT
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| seZ_RT | The standard error of the z-scored response latency. | numeric | 1 | 0.9997447 | 0.03 | 0.07 | 0.47 | 0.0747394 | 0.0258709 | ▇▁▁▁▁ |
The proportion of correct answers out of total answered for that item.
Distribution of values for accuracy
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| accuracy | The proportion of correct answers out of total answered for that item. | numeric | 0 | 1 | 0.0092 | 0.97 | 1 | 0.9470538 | 0.0771837 | ▁▁▁▁▇ |
The average raw response latency excluding outliers of 2.5 Z or higher.
Distribution of values for Z2.5_avgRT
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_avgRT | The average raw response latency excluding outliers of 2.5 Z or higher. | numeric | 0 | 1 | 383 | 838 | 1391 | 863.0617 | 164.703 | ▁▇▇▃▁ |
The average Z-scored response latency excluding outliers of 2.5 Z or higher.
Distribution of values for Z2.5_avgZ_RT
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_avgZ_RT | The average Z-scored response latency excluding outliers of 2.5 Z or higher. | numeric | 0 | 1 | -1.4 | -0.13 | 1.4 | -0.0748136 | 0.4520479 | ▁▇▇▃▁ |
The number of data points viewed that were not Z equal 2.5 or higher
Distribution of values for Z2.5_samplesize
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_samplesize | The number of data points viewed that were not Z equal 2.5 or higher | numeric | 0 | 1 | 1 | 121 | 363 | 122.1008 | 25.37061 | ▁▇▁▁▁ |
The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.
Distribution of values for Z2.5_n_answered
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_n_answered | The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation. | numeric | 0 | 1 | 1 | 121 | 363 | 122.1008 | 25.37061 | ▁▇▁▁▁ |
Standard error of the raw response latency after removing Z equal 2.5 or higher.
Distribution of values for Z2.5_seRT
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_seRT | Standard error of the raw response latency after removing Z equal 2.5 or higher. | numeric | 1 | 0.9997447 | 0.84 | 27 | 143 | 28.97971 | 9.645963 | ▇▆▁▁▁ |
Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.
Distribution of values for Z2.5_seZ_RT
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_seZ_RT | Standard error of the Z-scored response latency after removing Z equal 2.5 or higher. | numeric | 1 | 0.9997447 | 0.028 | 0.058 | 0.29 | 0.0607403 | 0.0161149 | ▇▁▁▁▁ |
The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.
Distribution of values for Z2.5_accuracy
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z2.5_accuracy | The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1. | numeric | 0 | 1 | 1 | 1 | 1 | 1 | 0 | ▁▁▇▁▁ |
The average raw response latency excluding outliers of 3.0 Z or higher.
Distribution of values for Z3.0_avgRT
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_avgRT | The average raw response latency excluding outliers of 3.0 Z or higher. | numeric | 0 | 1 | 383 | 844 | 1455 | 873.9104 | 175.4686 | ▁▇▇▃▁ |
The average Z-scored response latency excluding outliers of 3.0 Z or higher.
Distribution of values for Z3.0_avgZ_RT
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_avgZ_RT | The average Z-scored response latency excluding outliers of 3.0 Z or higher. | numeric | 0 | 1 | -1.4 | -0.12 | 1.7 | -0.0449096 | 0.4814571 | ▁▇▆▂▁ |
The number of data points viewed that were not Z equal 3.0 or higher
Distribution of values for Z3.0_samplesize
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_samplesize | The number of data points viewed that were not Z equal 3.0 or higher | numeric | 0 | 1 | 1 | 123 | 364 | 123.5933 | 25.04458 | ▁▇▁▁▁ |
The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.
Distribution of values for Z3.0_n_answered
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_n_answered | The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation. | numeric | 0 | 1 | 1 | 123 | 364 | 123.5933 | 25.04458 | ▁▇▁▁▁ |
Standard error of the raw response latency after removing Z equal 3.0 or higher.
Distribution of values for Z3.0_seRT
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_seRT | Standard error of the raw response latency after removing Z equal 3.0 or higher. | numeric | 1 | 0.9997447 | 0.84 | 28 | 143 | 29.9302 | 10.00673 | ▇▆▁▁▁ |
Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.
Distribution of values for Z3.0_seZ_RT
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_seZ_RT | Standard error of the Z-scored response latency after removing Z equal 3.0 or higher. | numeric | 1 | 0.9997447 | 0.028 | 0.061 | 0.35 | 0.0640459 | 0.0178888 | ▇▁▁▁▁ |
The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.
Distribution of values for Z3.0_accuracy
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Z3.0_accuracy | The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1. | numeric | 0 | 1 | 1 | 1 | 1 | 1 | 0 | ▁▁▇▁▁ |
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "Semantic Priming Across Many Languages Item Level Data (Example using Serbian data)",
"description": "This dataset includes the summarized item data from the SPAML project (example is specifically Serbian, but all files are structured the same way). The data is averaged over items, regardless of condition for words (i.e., related or unrelated trial). \n\nSemantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"identifier": "https://doi.org/10.5281/zenodo.10888833",
"creator": "Erin M. Buchanan",
"citation": "Buchanan, E., Cuccolo, K., Heyman, T., Iyer, A., Coles, N., Lewis Jr, N., Peters, K., van Berkel, N., Taylor, J., Van't Veer, A. E., Montefinese, M., Valentine, K. D., Maxwell, N., Türkan, B. N., Williams, G., Oliveros-Chacana, J. C., Röer, J., Fini, C., Acar, O., … Lewis, S. C. (2024). SemanticPriming/SPAML: SPAML v1 Data Release (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10888833",
"url": "https://github.com/SemanticPriming/SPAML/releases/",
"datePublished": "2024-05-01",
"temporalCoverage": "2022-2024",
"spatialCoverage": "Online",
"keywords": ["word", "class", "avgRT", "avgZ_RT", "samplesize", "n_answered", "seRT", "seZ_RT", "accuracy", "Z2.5_avgRT", "Z2.5_avgZ_RT", "Z2.5_samplesize", "Z2.5_n_answered", "Z2.5_seRT", "Z2.5_seZ_RT", "Z2.5_accuracy", "Z3.0_avgRT", "Z3.0_avgZ_RT", "Z3.0_samplesize", "Z3.0_n_answered", "Z3.0_seRT", "Z3.0_seZ_RT", "Z3.0_accuracy"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "word",
"description": "Original stimulus showed to the participant in the language of the study.",
"@type": "propertyValue"
},
{
"name": "class",
"description": "Word/nonword trial type indicator, which tells you what the correct answer is for the trial.",
"@type": "propertyValue"
},
{
"name": "avgRT",
"description": "Average response latency for the stimulus item across all participants.",
"@type": "propertyValue"
},
{
"name": "avgZ_RT",
"description": "Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.",
"@type": "propertyValue"
},
{
"name": "samplesize",
"description": "The number of participants who were shown that item.",
"@type": "propertyValue"
},
{
"name": "n_answered",
"description": "The number of participants who answered that item (i.e., it did not time out.",
"@type": "propertyValue"
},
{
"name": "seRT",
"description": "The standard error of the raw response latency.",
"@type": "propertyValue"
},
{
"name": "seZ_RT",
"description": "The standard error of the z-scored response latency.",
"@type": "propertyValue"
},
{
"name": "accuracy",
"description": "The proportion of correct answers out of total answered for that item.",
"@type": "propertyValue"
},
{
"name": "Z2.5_avgRT",
"description": "The average raw response latency excluding outliers of 2.5 Z or higher.",
"@type": "propertyValue"
},
{
"name": "Z2.5_avgZ_RT",
"description": "The average Z-scored response latency excluding outliers of 2.5 Z or higher.",
"@type": "propertyValue"
},
{
"name": "Z2.5_samplesize",
"description": "The number of data points viewed that were not Z equal 2.5 or higher",
"@type": "propertyValue"
},
{
"name": "Z2.5_n_answered",
"description": "The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
"@type": "propertyValue"
},
{
"name": "Z2.5_seRT",
"description": "Standard error of the raw response latency after removing Z equal 2.5 or higher.",
"@type": "propertyValue"
},
{
"name": "Z2.5_seZ_RT",
"description": "Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.",
"@type": "propertyValue"
},
{
"name": "Z2.5_accuracy",
"description": "The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.",
"@type": "propertyValue"
},
{
"name": "Z3.0_avgRT",
"description": "The average raw response latency excluding outliers of 3.0 Z or higher.",
"@type": "propertyValue"
},
{
"name": "Z3.0_avgZ_RT",
"description": "The average Z-scored response latency excluding outliers of 3.0 Z or higher.",
"@type": "propertyValue"
},
{
"name": "Z3.0_samplesize",
"description": "The number of data points viewed that were not Z equal 3.0 or higher",
"@type": "propertyValue"
},
{
"name": "Z3.0_n_answered",
"description": "The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
"@type": "propertyValue"
},
{
"name": "Z3.0_seRT",
"description": "Standard error of the raw response latency after removing Z equal 3.0 or higher.",
"@type": "propertyValue"
},
{
"name": "Z3.0_seZ_RT",
"description": "Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.",
"@type": "propertyValue"
},
{
"name": "Z3.0_accuracy",
"description": "The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.",
"@type": "propertyValue"
}
]
}`